code: def on_after_batch_transfer(
      self, batch: Any, dataloader_idx: int
  ) -> Any:
      current_stage = self.trainer.state.stage
      if (
          current_stage == RunningStage.VALIDATING
          or current_stage == RunningStage.PREDICTING
      ):
          self.trainer.state.stage = RunningStage.TRAINING
      batch = super().on_after_batch_transfer(batch, dataloader_idx)
      self.trainer.state.stage = current_stage
      return batch
  
imports: import warnings
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  import torch
  from torch import Tensor
  from torch.utils.data import DataLoader, Dataset, random_split
  from flash.core.data.data_module import DataModule
  from flash.core.data.io.input import InputBase
  from flash.core.data.io.input_transform import create_worker_input_transform_processor
  from flash.core.utilities.imports import _BAAL_AVAILABLE, requires
  from flash.core.utilities.stages import RunningStage
label_desc: Use image node_id 10 for calling the ActiveLearningDataModule obj's underlying member method code with example usage: obj.on_after_batch_transfer(batch, dataloader_idx) and returns: batch